Identifying key multifunctional components shared by critical cancer and normal liver pathways via SparseGMM.
Ontology highlight
ABSTRACT:
SUBMITTER: Bakr S
PROVIDER: S-EPMC9939431 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Publications
Identifying key multifunctional components shared by critical cancer and normal liver pathways via SparseGMM.
Bakr Shaimaa S Brennan Kevin K Mukherjee Pritam P Argemi Josepmaria J Hernaez Mikel M Gevaert Olivier O
Cell reports methods 20230116 1
Despite the abundance of multimodal data, suitable statistical models that can improve our understanding of diseases with genetic underpinnings are challenging to develop. Here, we present SparseGMM, a statistical approach for gene regulatory network discovery. SparseGMM uses latent variable modeling with sparsity constraints to learn Gaussian mixtures from multiomic data. By combining coexpression patterns with a Bayesian framework, SparseGMM quantitatively measures confidence in regulators and ...[more]
Similar Datasets
Project description:BackgroundThe coupling of pathways and processes through shared components is being increasingly recognised as a common theme which occurs in many cell signalling contexts, in which it plays highly non-trivial roles.ResultsIn this paper we develop a basic modelling and systems framework in a general setting for understanding the coupling of processes and pathways through shared components. Our modelling framework starts with the interaction of two components with a common third component and includes production and degradation of all these components. We analyze the signal processing in our model to elucidate different aspects of the coupling. We show how different kinds of responses, including "ultrasensitive" and adaptive responses, may occur in this setting. We then build on the basic model structure and examine the effects of additional control regulation, switch-like signal processing, and spatial signalling. In the process, we identify a way in which allosteric regulation may contribute to signalling specificity, and how competitive effects may allow an enzyme to robustly coordinate and time the activation of parallel pathways.ConclusionsWe have developed and analyzed a common systems platform for examining the effects of coupling of processes through shared components. This can be the basis for subsequent expansion and understanding the many biologically observed variations on this common theme.
| S-EPMC3162518 | biostudies-literature
Project description:ObjectivesTo (1) provide an up-to-date overview of shared decision making (SDM)-models, (2) give insight in the prominence of components present in SDM-models, (3) describe who is identified as responsible within the components (patient, healthcare professional, both, none), (4) show the occurrence of SDM-components over time, and (5) present an SDM-map to identify SDM-components seen as key, per healthcare setting.DesignSystematic review.Eligibility criteriaPeer-reviewed articles in English presenting a new or adapted model of SDM.Information sourcesAcademic Search Premier, Cochrane, Embase, Emcare, PsycINFO, PubMed, and Web of Science were systematically searched for articles published up to and including September 2, 2019.ResultsForty articles were included, each describing a unique SDM-model. Twelve models were generic, the others were specific to a healthcare setting. Fourteen were based on empirical data, 26 primarily on analytical thinking. Fifty-three different elements were identified and clustered into 24 components. Overall, Describe treatment options was the most prominent component across models. Components present in >50% of models were: Make the decision (75%), Patient preferences (65%), Tailor information (65%), Deliberate (58%), Create choice awareness (55%), and Learn about the patient (53%). In the majority of the models (27/40), both healthcare professional and patient were identified as actors. Over time, Describe treatment options and Make the decision are the two components which are present in most models in any time period. Create choice awareness stood out for being present in a markedly larger proportion of models over time.ConclusionsThis review provides an up-to-date overview of SDM-models, showing that SDM-models quite consistently share some components but that a unified view on what SDM is, is still lacking. Clarity about what SDM constitutes is essential though for implementation, assessment, and research purposes. A map is offered to identify SDM-components seen as key.Trial registrationPROSPERO registration CRD42015019740.
| S-EPMC6937101 | biostudies-literature
Project description:Liver diseases are important causes of morbidity and mortality worldwide. The aim of this study was to identify differentially expressed microRNAs (miRNAs), target genes, and key pathways as innovative diagnostic biomarkers in liver patients with different pathology and functional state. We determined, using RT-qPCR, the expression of 472 miRNAs in 125 explanted livers from subjects with six different liver pathologies and from control livers. ANOVA was employed to obtain differentially expressed miRNAs (DEMs), and miRDB (MicroRNA target prediction database) was used to predict target genes. A miRNA-gene differential regulatory (MGDR) network was constructed for each condition. Key miRNAs were detected using topological analysis. Enrichment analysis for DEMs was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). We identified important DEMs common and specific to the different patient groups and disease progression stages. hsa-miR-1275 was universally downregulated regardless the disease etiology and stage, while hsa-let-7a*, hsa-miR-195, hsa-miR-374, and hsa-miR-378 were deregulated. The most significantly enriched pathways of target genes controlled by these miRNAs comprise p53 tumor suppressor protein (TP53)-regulated metabolic genes, and those involved in regulation of methyl-CpG-binding protein 2 (MECP2) expression, phosphatase and tensin homolog (PTEN) messenger RNA (mRNA) translation and copper homeostasis. Our findings show a novel panel of deregulated miRNAs in the liver tissue from patients with different liver pathologies. These miRNAs hold potential as biomarkers for diagnosis and staging of liver diseases.
| S-EPMC7582243 | biostudies-literature
Project description:In prion disease, direct interaction between the cellular prion protein (PrP(C)) and its misfolded disease-associated conformer PrP(Sc) is a crucial, although poorly understood step promoting the formation of nascent PrP(Sc) and prion infectivity. Recently, we hypothesized that three regions of PrP (corresponding to amino acid residues 23-33, 98-110, and 136-158) interacting specifically and robustly with PrP(Sc), likely represent peptidic components of one flank of the prion replicative interface. In this study, we created epitope-tagged mouse PrP(C) molecules in which the PrP sequences 23-33, 98-110, and 136-158 were modified. These novel PrP molecules were individually expressed in the prion-infected neuroblastoma cell line (ScN2a) and the conversion of each mutated mouse PrP(C) substrate to PrP(Sc) compared with that of the epitope-tagged wild-type mouse PrP(C). Mutations within PrP 98-110, substituting all 4 wild-type lysine residues with alanine residues, prevented conversion to PrP(Sc). Furthermore, when residues within PrP 136-140 were collectively scrambled, changed to alanines, or amino acids at positions 136, 137, and 139 individually replaced by alanine, conversion to PrP(Sc) was similarly halted. However, other PrP molecules containing mutations within regions 23-33 and 101-104 were able to readily convert to PrP(Sc). These results suggest that PrP sequence comprising residues 98-110 and 136-140 not only participates in the specific binding interaction between PrP(C) and PrP(Sc), but also in the process leading to conversion of PrP(Sc)-sequestered PrP(C) into its disease-associated form.
| S-EPMC2590689 | biostudies-literature
Project description:MotivationSeveral types of studies, including genome-wide association studies and RNA interference screens, strive to link genes to diseases. Although these approaches have had some success, genetic variants are often only present in a small subset of the population, and screens are noisy with low overlap between experiments in different labs. Neither provides a mechanistic model explaining how identified genes impact the disease of interest or the dynamics of the pathways those genes regulate. Such mechanistic models could be used to accurately predict downstream effects of knocking down pathway members and allow comprehensive exploration of the effects of targeting pairs or higher-order combinations of genes.ResultsWe developed methods to model the activation of signaling and dynamic regulatory networks involved in disease progression. Our model, SDREM, integrates static and time series data to link proteins and the pathways they regulate in these networks. SDREM uses prior information about proteins' likelihood of involvement in a disease (e.g. from screens) to improve the quality of the predicted signaling pathways. We used our algorithms to study the human immune response to H1N1 influenza infection. The resulting networks correctly identified many of the known pathways and transcriptional regulators of this disease. Furthermore, they accurately predict RNA interference effects and can be used to infer genetic interactions, greatly improving over other methods suggested for this task. Applying our method to the more pathogenic H5N1 influenza allowed us to identify several strain-specific targets of this infection.AvailabilitySDREM is available from http://sb.cs.cmu.edu/sdrem.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3694658 | biostudies-literature
Project description:IntroductionThe arrest of rolling T lymphocytes at specific locations is crucial to proper immune response function. We previously developed a model of chemokine-driven integrin activation, termed integrative signaling adhesive dynamics (ISAD). In addition, we have shown that loss of diacylglycerol kinase (DGK) leads to a gain of function regarding adhesion under shear flow. We undertook this study to understand the sensitivity of adhesion to perturbations in other signaling molecules.MethodsWe adapted multi-parametric sensitivity analysis (MPSA) for use in our ISAD model to identify important parameters, including initial protein concentrations and kinetic rate constants, for T lymphocyte arrest. We also compared MPSA results to those obtained from a single parametric sensitivity analysis.ResultsIn addition to the previously shown importance of DGK in lymphocyte arrest, PIP2 cleavage and Rap1 activation are crucial in determining T cell arrest dynamics, which agree with previous experimental findings. The l-selectin density on the T lymphocyte surface also plays a large role in determining the distance rolled before arrest. Both the MPSA and single-parametric method returned similar results regarding the most sensitive kinetic rate constants.ConclusionWe show here that the regulation of the amount of second messengers are, in general, more critical for determining T lymphocyte arrest over the initial signaling proteins, highlighting the importance of amplification of signaling in cell adhesion responses. Overall, this work provides a mechanistic insight of the contribution of key pathways and components, thus may help to identify potential therapeutic targets for drug development against immune disorders.
| S-EPMC6816696 | biostudies-literature
Project description:Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research.
| S-EPMC5051932 | biostudies-literature
Project description:BACKGROUND: Residents demonstrate scholarly activity by presenting posters at academic meetings. Although recommendations from national organizations are available, evidence identifying which components are most important is not. OBJECTIVE: To develop and test an evaluation tool to measure the quality of case report posters and identify the specific components most in need of improvement. DESIGN: Faculty evaluators reviewed case report posters and provided on-site feedback to presenters at poster sessions of four annual academic general internal medicine meetings. A newly developed ten-item evaluation form measured poster quality for specific components of content, discussion, and format (5-point Likert scale, 1 = lowest, 5 = highest). MAIN OUTCOME MEASURE(S): Evaluation tool performance, including Cronbach alpha and inter-rater reliability, overall poster scores, differences across meetings and evaluators and specific components of the posters most in need of improvement. RESULTS: Forty-five evaluators from 20 medical institutions reviewed 347 posters. Cronbach's alpha of the evaluation form was 0.84 and inter-rater reliability, Spearman's rho 0.49 (p < 0.001). The median score was 4.1 (Q1 -Q3, 3.7-4.6)(Q1 = 25th, Q3 = 75th percentile). The national meeting median score was higher than the regional meetings (4.4 vs, 4.0, P < 0.001). We found no difference in faculty scores. The following areas were identified as most needing improvement: clearly state learning objectives, tie conclusions to learning objectives, and use appropriate amount of words. CONCLUSIONS: Our evaluation tool provides empirical data to guide trainees as they prepare posters for presentation which may improve poster quality and enhance their scholarly productivity.
| S-EPMC2642558 | biostudies-other
Project description:Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that are learned from the data. As with any statistic, a concern with using ICA is the degree to which the estimated ICs are reliable. An IC may not be reliable if ICA was trained on insufficient data, if ICA training was stopped prematurely or at a local minimum (for some algorithms), or if multiple global minima were present. Consequently, evidence of ICA reliability is critical for the credibility of ICA results. In this paper, we present a new algorithm for assessing the reliability of ICs based on applying ICA separately to split-halves of a data set. This algorithm improves upon existing methods in that it considers both IC scalp topographies and activations, uses a probabilistically interpretable threshold for accepting ICs as reliable, and requires applying ICA only three times per data set. As evidence of the method's validity, we show that the method can perform comparably to more time intensive bootstrap resampling and depends in a reasonable manner on the amount of training data. Finally, using the method we illustrate the importance of checking the reliability of ICs by demonstrating that IC reliability is dramatically increased by removing the mean EEG at each channel for each epoch of data rather than the mean EEG in a prestimulus baseline.
| S-EPMC3062525 | biostudies-literature
Project description:BackgroundPanax ginseng C. A. Mey (ginseng) is a traditional Chinese medicinal herb used for the treatment of nervous system disorders, such as Alzheimer's disease (AD). However, the pharmacological mechanisms of ginseng involved in AD have not been systematically investigated. Here, a network pharmacology approach was adopted to explore the effective components, core targets, and key pathways of ginseng against AD.MethodsTCMSP database was used to screen the active ingredients of ginseng. Prediction of the targets of ginseng and AD-related genes was performed using online public databases. "Compound-Target," "Compound-Target-Disease," "Protein-Protein Interaction (PPI)," "Compound-Target-Pathway," and "Compound-Target-GO-Pathway" networks were constructed with Cytoscape 3.7.2 software. Gene Ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed by using the DAVID database.ResultsA total of 22 bioactive compounds were identified from ginseng, and 481 targets of ginseng and 763 AD-related targets were obtained from public databases. The PPI network screened out 19 hub genes of ginseng against AD. According to GO function enrichment, ginseng influenced cell proliferation, death, the nitric oxide biosynthetic process, hypoxia response, and synaptic transmission. Neuroactive ligand-receptor interaction, serotonergic synapse, calcium signaling, cAMP signaling, FoxO signaling, Ras signaling, and PI3K-AKT signaling were among the most key regulatory pathways. The compound-target-GO-route network found EGFR, MAPK1, MAPK14, AKT1, CASP3, and PRKACA as key genes, with PI3K-AKT signaling being the most important pathway for ginseng's anti-AD activity.ConclusionGinseng exerts neuroprotective effects in AD patients through multicomponent, multitarget, and multipathway modes, providing novel insight into the pharmacological and experimental research on ginseng against AD.
| S-EPMC9886485 | biostudies-literature